scholarly journals SPATIOTEMPORAL CHANGE OF URBAN AGRICULTURE USING GOOGLE EARTH IMAGERY: A CASE OF MUNICIPALITY OF NAKHONRATCHASIMA CITY, THAILAND

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1301-1306
Author(s):  
Y. Jantakat ◽  
P. Juntakut ◽  
S. Plaiklang ◽  
W. Arree ◽  
C. Jantakat
Keyword(s):  
Urban Agriculture ◽  
Land Development ◽  
Temporal Analysis ◽  
Rice Paddy ◽  
Google Earth ◽  
Field Crop ◽  
Land Use Classification ◽  
Change Analysis ◽  
Entire Study ◽  
Spatiotemporal Change

<p><strong>Abstract.</strong> Presently, urban agriculture (UA) is an important part of the urban ecosystem and a key factor that can help in the urban environmental management. Therefore, this paper studies a spatial-temporal analysis of UA areas and types in Municipality of Nakhonratchasima City (MNC), Thailand. This UA types referred land use classification system of Land Development Department (LDD). Google Earth images acquired in the years of 2007, 2011, 2015 and 2018 were used to examine UA change with segmentation-based classification method in QGIS to classify Google Earth images into thematic maps. Moreover, this study showed different spatiotemporal change patterns, composition and rates in the study area and indicates the importance of analyzing UA change. Therefore, the results of this classification consisted of eleven classes – abandoned paddy field, rice paddy, abandoned field crop, mixed field crop, cassava, betel palm, mixed orchard, coconut, rose apple, truck crop, and fish farm. Truck crop had the greatest cover in study area while floricultural covered the minimal space over periods of study. The UA change analysis over time for entire study areas provides an overall picture of change trends. Furthermore, the UA change at census sector scale gives new insights on how human-induced activities (e.g., built-up areas and roads) affect UA change patterns and rates. This research indicates the necessity to implement change detection for better understanding the UA change patterns and rates.</p>

scholarly journals Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park

2021 ◽  
Vol 13 (12) ◽  
pp. 2299
Author(s):  
Andrea Tassi ◽  
Daniela Gigante ◽  
Giuseppe Modica ◽  
Luciano Di Martino ◽  
Marco Vizzari
Keyword(s):  
Land Cover ◽  
National Park ◽  
Time Window ◽  
Central Italy ◽  
Google Earth ◽  
Data Cube ◽  
Landsat 8 ◽  
Change Analysis ◽  
Object Based ◽  
Google Earth Engine

With the general objective of producing a 2018–2020 Land Use/Land Cover (LULC) map of the Maiella National Park (central Italy), useful for a future long-term LULC change analysis, this research aimed to develop a Landsat 8 (L8) data composition and classification process using Google Earth Engine (GEE). In this process, we compared two pixel-based (PB) and two object-based (OB) approaches, assessing the advantages of integrating the textural information in the PB approach. Moreover, we tested the possibility of using the L8 panchromatic band to improve the segmentation step and the object’s textural analysis of the OB approach and produce a 15-m resolution LULC map. After selecting the best time window of the year to compose the base data cube, we applied a cloud-filtering and a topography-correction process on the 32 available L8 surface reflectance images. On this basis, we calculated five spectral indices, some of them on an interannual basis, to account for vegetation seasonality. We added an elevation, an aspect, a slope layer, and the 2018 CORINE Land Cover classification layer to improve the available information. We applied the Gray-Level Co-Occurrence Matrix (GLCM) algorithm to calculate the image’s textural information and, in the OB approaches, the Simple Non-Iterative Clustering (SNIC) algorithm for the image segmentation step. We performed an initial RF optimization process finding the optimal number of decision trees through out-of-bag error analysis. We randomly distributed 1200 ground truth points and used 70% to train the RF classifier and 30% for the validation phase. This subdivision was randomly and recursively redefined to evaluate the performance of the tested approaches more robustly. The OB approaches performed better than the PB ones when using the 15 m L8 panchromatic band, while the addition of textural information did not improve the PB approach. Using the panchromatic band within an OB approach, we produced a detailed, 15-m resolution LULC map of the study area.

scholarly journals Multitemporal Geospatial Evaluation of Urban Agriculture and (Non)-Sustainable Food Self-Provisioning in Milan, Italy

2019 ◽  
Vol 11 (7) ◽  
pp. 1846 ◽  
Author(s):  
Giuseppe Pulighe ◽  
Flavio Lupia
Keyword(s):  
Urban Agriculture ◽  
Food Production ◽  
Food Systems ◽  
Arable Land ◽  
Well Being ◽  
Google Earth ◽  
Total Production ◽  
Ancillary Data ◽  
Sustainable Food ◽  
The City

Urban agriculture in Global North cities is strongly promoted as a sustainable solution to achieve different goals, such as food production, quality of life, and well-being. Although several attempts have been made to evaluate urban agriculture production, few studies have investigated food production in a multitemporal geospatial way and considered per capita population needs, gender, and age strata consumption. This study presents a spatiotemporal quantification of urban agriculture in the city of Milan (Italy) for assessing food self-provisioning potential. We utilized high-resolution Google Earth images and ancillary data to create a detailed cadaster of urban agriculture for the years 2007 and 2014. Based on four scenarios of food production and statistical data on vegetables and cereals consumption, we estimated current total production and requirements for the city dwellers. Our results showed that the actual extension of vegetable gardens (98 ha) and arable land (2539 ha) in the best scenario could satisfy approximately 63,700 and 321,000 consumers of vegetables and cereal products, respectively. Overall, current urban agriculture production is not able to meet vegetables and cereal consumption for more than 1.3 million city residents. Scenario estimates suggest rethinking land use promoting horticultural production to achieve more sustainable food systems.

20 years of forest change in Natura 2000 protected areas network

2020 ◽  
Author(s):  
Marinela-Adriana Chețan ◽  
Andrei Dornik
Keyword(s):  
Protected Areas ◽  
Satellite Data ◽  
Forest Cover ◽  
Natura 2000 ◽  
Temporal Analysis ◽  
Mean Value ◽  
Google Earth ◽  
Forest Change ◽  
Forest Protection ◽  
Forest Area Change

&lt;p&gt;Natura 2000 network, the world's largest network of protected areas, is considered a success for habitat and biodiversity protection, in the last decades. Our objective is to develop an algorithm for satellite data temporal analysis of protected areas, and to apply subsequently this algorithm for analysis of all Natura 2000 sites in Europe. We have developed an algorithm for satellite data temporal analysis of protected areas using JavaScript in Google Earth Engine, which is a web interface for the massive analysis of geospatial data, providing access to huge amount of data and facilitating development of complex workflows. This work focused on analysis of Global Forest Change dataset&amp;#160;representing forest change, at 30 meters resolution, globally, between 2000 and 2018. Our results show that at least regarding forest protection, the network is not very successful, the 25350 sites losing 35246.8 km&lt;sup&gt;2&lt;/sup&gt; of forest cover between 2000 and 2018, gaining only 9862.1 km&lt;sup&gt;2&lt;/sup&gt;. All 28 countries recorded a negative forest net change, with a mean value of -906.6 km&lt;sup&gt;2&lt;/sup&gt;, the largest forest area change recording Spain (-5106.4 km&lt;sup&gt;2&lt;/sup&gt; in 1631 sites), Poland (-4529 km&lt;sup&gt;2&lt;/sup&gt; in 962 sites), Portugal (-2781.9 km&lt;sup&gt;2&lt;/sup&gt; in 120 sites), Romania (-1601.4 km&lt;sup&gt;2&lt;/sup&gt; in 569 sites), Germany (-1365.7 km&lt;sup&gt;2&lt;/sup&gt; in 5049 sites) and France (-1270.9 km&lt;sup&gt;2&lt;/sup&gt; in 1520 sites). Among countries with the lowest values in net forest change is Ireland (-17.4 km&lt;sup&gt;2&lt;/sup&gt; in 447 sites), Estonia (-104.1 km&lt;sup&gt;2&lt;/sup&gt; in 518 sites), Netherlands (-132.3 km&lt;sup&gt;2&lt;/sup&gt; in 152 sites), Finland (-268.6 km&lt;sup&gt;2&lt;/sup&gt; in 1722 sites) and Sweden (-341.6 km&lt;sup&gt;2&lt;/sup&gt; in 3786 sites).&lt;/p&gt;

Dormant and Active Landslides Classification Using Machine Learning Algorithms Coupled With Geological Field Inspection: Pohang Case Study

2020 ◽  
Author(s):  
Omar F. Althuwaynee ◽  
In-Tak Hwang ◽  
Hyuck-jin Park ◽  
Swang-Wan Kim ◽  
Ali Aydda
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Field Work ◽  
Google Earth ◽  
Aerial Photographs ◽  
Susceptibility Map ◽  
Alluvial Deposits ◽  
Basic Field ◽  
Entire Study ◽  
Extreme Gradient Boosting

&lt;p&gt;In 1998, intense rainfall events hit the Pohang state, south west of Korea, which results in highest number of landslides registered in this area (generally the area has a relatively short history of landslide inventorying). The current inventory was digitized using Aerial photographs (lack of photogeological stereoscopic analysis of the aerial images) and coupled with basic field verification (due to limit funding available). Leaving the applied susceptibility maps models performed, using this inventory, with high degree of uncertainty.&amp;#160; Currently a research initiative carried to audit the landslide inventory using freely available aerial photographs and the time tuning function in Google earth for aerial archives. We notice some slopes area covered with deformed forest types that is similar in texture to that seen in digitized locations of landslides inventory. Due to long retune period of similar rainfall event, and with an assumption that the available landslides inventory might not complete. A certain hypothesis of additional investigation including field work to audit the landslides incidents is highly needed. In the current research, we assumed that, some dormant slopes caused by the 1998 event can be reactivated with the current extreme (uncontrolled) uses of slopes by human activities (constructions of real estate&amp;#8217;s projects). To that end, a methodology of three main stages were proposed.&lt;/p&gt;&lt;p&gt;Stage one; Dormant susceptibility map (DSM) coupled with landslide susceptibility map will be produced. Machine learning supervised classification of eXtreme Gradient Boosting algorithms and Ensemble Random Forest, that run on tree-based classification assumption considering only active and dormant landslides as well as stable ground. Stage two; field work needs to be designed by geological and geotechnical experts to collect the doubtful locations by guidance of DSM and consider the new locations as dormant inventory. However, the areas of low dormant susceptibility (or mutual zones with Landslide susceptibility) will be recommended for advanced filed work and soil sampling test to complete the landslides identification of such highly urbanized area.&amp;#160;Stage three; knowing the contour depths of diluvial and alluvial deposits can be useful for extracting areas that are more prone to landslides. Especially in the case of a rigid bedrock beneath the diluvial crust. Therefore, reconstructing the Quaternary formation thickness using boreholes repository and then represent the entire study area using CoKriging surface interpolation technique with elevation model. The current research results will provide us a better understanding of landcover stability conditions and their spatial prediction features.&lt;/p&gt;&lt;div&gt; &lt;div&gt;&amp;#160;&lt;/div&gt; &lt;div&gt;[email protected]&lt;/div&gt; &lt;div&gt;[email protected]&lt;/div&gt; &lt;/div&gt;

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